Sensor intelligent operation and maintenance service optimization method and system combined with Internet of Things

Abstract

The invention provides a sensor intelligent operation and maintenance service optimization method and system combined with the Internet of Things, and the method comprises the steps: receiving real-time operation feedback information which is transmitted by an Internet of Things sensor cluster and comprises an operation state and equipment linkage feedback content, and calling a pre-trained sensor operation and maintenance analysis model to mine an operation and maintenance demand; and generating a corresponding operation and maintenance demand association relationship, and integrating to form an operation and maintenance demand set. And associating preset operation and maintenance resource pool information based on the operation and maintenance demand set, generating an adaptive operation and maintenance resource configuration scheme, pushing the adaptive operation and maintenance resource configuration scheme to an operation and maintenance execution end, and receiving execution feedback information. According to the method, the operation and maintenance requirements of the Internet of Things sensor cluster can be precisely processed, the operation and maintenance resources are reasonably allocated, the operation and maintenance efficiency, accuracy and reliability of the Internet of Things sensor cluster are effectively improved, and stable operation of related systems is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and more specifically, to a method and system for optimizing intelligent operation and maintenance services for sensors that integrates the Internet of Things. Background Technology

[0002] In today's industrial production and intelligent management fields, IoT sensors are widely used in various devices and systems to collect real-time data on equipment operating status and environmental information. As the scale of IoT sensor clusters continues to expand, their operation and maintenance management faces numerous challenges. Currently, the operation and maintenance of IoT sensors mainly relies on regular manual inspections and alarm mechanisms based on simple rules. Manual inspections are not only inefficient but also struggle to capture subtle changes and potential faults in sensors in real time. Alarm mechanisms based on simple rules often only make judgments for single sensors or simple scenarios, failing to comprehensively consider the interrelationships between sensors in the cluster and the interconnectivity of devices. This leads to problems of untimely and inaccurate maintenance in actual operation and maintenance, failing to effectively ensure the stable operation of IoT sensor clusters, and consequently affecting the performance and reliability of the entire related system or equipment. Therefore, there is an urgent need for a method that can efficiently and intelligently handle the operation and maintenance needs of IoT sensor clusters. Summary of the Invention

[0003] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for optimizing intelligent operation and maintenance services for sensors in conjunction with the Internet of Things, the method comprising: Receive real-time sensor operation feedback information transmitted by the Internet of Things sensor cluster. The real-time sensor operation feedback information includes the sensor's operation status feedback content and the linkage feedback content of the sensor's associated devices. The pre-trained sensor operation and maintenance analysis model is invoked to mine and process the operation and maintenance requirements of the real-time operation feedback information of the sensors, and generate the operation and maintenance requirement association relationship corresponding to the IoT sensor cluster. The operation and maintenance requirements of the IoT sensor cluster are integrated based on the relationship between the operation and maintenance requirements of the IoT sensor cluster. Based on the set of operation and maintenance requirements of IoT sensor clusters, and the information of the preset operation and maintenance resource pool, an operation and maintenance resource configuration scheme adapted to IoT sensor clusters is generated. Push the operation and maintenance resource configuration plan adapted to the IoT sensor cluster to the operation and maintenance execution terminal and receive the operation and maintenance plan execution feedback information returned by the operation and maintenance execution terminal.

[0004] In another aspect, embodiments of the present invention also provide a sensor intelligent operation and maintenance service optimization system combined with the Internet of Things, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions or code. The processor is used to execute the programs, instructions or code in the machine-readable storage medium to implement the above-mentioned method.

[0005] Based on the above, this embodiment of the invention receives real-time operational feedback information from an IoT sensor cluster, including operational status and device linkage feedback. It then utilizes a pre-trained sensor operation and maintenance analysis model to mine operation and maintenance requirements, enabling comprehensive and accurate generation of operation and maintenance requirement relationships corresponding to the IoT sensor cluster. The operation and maintenance requirement set formed based on these relationships comprehensively considers factors such as the sensor itself and device linkage, making the operation and maintenance requirements more complete and accurate. Based on the operation and maintenance requirement set and associated with pre-set operation and maintenance resource pool information, a suitable operation and maintenance resource configuration scheme is generated, enabling reasonable allocation of operation and maintenance personnel, tools, and materials to ensure efficient operation and maintenance. Pushing the operation and maintenance resource configuration scheme to the operation and maintenance execution end and receiving execution feedback information allows for timely adjustments to the operation and maintenance strategy. Overall, this method significantly improves the efficiency, accuracy, and reliability of IoT sensor cluster operation and maintenance, effectively ensuring the stable operation of the IoT sensor cluster and related systems. Attached Figure Description

[0006] Figure 1 This is a schematic diagram of the execution flow of the sensor intelligent operation and maintenance service optimization method combined with the Internet of Things provided in the embodiments of the present invention.

[0007] Figure 2 This is a schematic diagram of exemplary hardware and software components of the sensor intelligent operation and maintenance service optimization system that combines the Internet of Things, provided in an embodiment of the present invention. Detailed Implementation

[0008] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating an embodiment of the sensor intelligent operation and maintenance service optimization method combined with the Internet of Things (IoT) provided by the present invention. The following is a detailed description of the sensor intelligent operation and maintenance service optimization method combined with the IoT.

[0009] Step S110: Receive real-time sensor operation feedback information transmitted from the IoT sensor cluster. This real-time sensor operation feedback information includes sensor operation status feedback and sensor-associated device linkage feedback. In the chemical production plant scenario of this embodiment, each sensor in the IoT sensor cluster transmits real-time sensor operation feedback information to the operation and maintenance management platform at set time intervals or when a status change is detected. For example, the sensor operation status feedback includes information such as the current detected temperature value, sensor battery level, and signal strength from a temperature sensor; and the current pressure detection value, sensor operating time, and any abnormal fluctuations from a pressure sensor. The sensor-associated device linkage feedback includes information such as the activation of a cooling device when a temperature sensor detects that the temperature of a reactor exceeds a threshold, and the associated cooling device is activated. The temperature sensor then provides information such as the linkage trigger signal, activation time, and operating power of the cooling device. Similarly, when a flow sensor detects an abnormal flow in a pipe, the associated valve adjusts its opening, and the flow sensor provides feedback on the valve adjustment range, the changed flow rate after adjustment, and the valve's operating status. These feedback messages are transmitted to the operation and maintenance management platform in the form of data packets via the Internet of Things (IoT) communication network. The platform's information receiving module parses these packets and extracts the operational status feedback and device linkage feedback.

[0010] Step S120: Call the pre-trained sensor operation and maintenance analysis model to mine and process the operation and maintenance requirements of the real-time operation feedback information of the sensors, and generate the operation and maintenance requirement association relationship corresponding to the IoT sensor cluster.

[0011] In this embodiment, the pre-trained sensor operation and maintenance analysis model is trained based on a large amount of chemical production sensor operation and maintenance data. The model structure includes a feedback information parsing layer, a feature association layer, a demand mapping layer, and a relationship mining layer. The sub-steps of this step are described in detail below: Step S121: Input the real-time operation feedback information of the sensor into the feedback information parsing layer of the sensor operation and maintenance analysis model. Perform layered parsing processing on the real-time operation feedback information of the sensor to obtain the state description information corresponding to the sensor operation status feedback content and the linkage description information corresponding to the linkage feedback content of the sensor-associated equipment. In the chemical production scenario of this embodiment, the feedback information parsing layer processes the received real-time operation feedback information of the sensor. For the sensor operation status feedback content, such as the operation status feedback content of the temperature sensor, the parsing layer performs semantic analysis and structured processing on data such as temperature detection value, power status, and signal strength status to extract key information that can describe the sensor operation status and form state description information. For example, information such as the temperature value change trend, remaining power ratio, and signal strength level are organized to form a description of the temperature sensor's operation status. For sensor-related device linkage feedback, such as the linkage feedback between a temperature sensor and a cooling device, the parsing layer analyzes data such as the linkage trigger signal, the cooling device's startup duration, and operating power to extract key interaction information and form linkage description information. This includes summarizing the cooling device's startup reason (triggered by the temperature value detected by the temperature sensor), the corresponding time range of startup duration, and changes in operating power, thus creating a description of the linkage between the temperature sensor and the cooling device. The feedback information parsing layer uses natural language processing technology and data parsing algorithms to perform word segmentation, part-of-speech tagging, and entity recognition on the input real-time sensor feedback information, transforming unstructured or semi-structured feedback information into structured state description information and linkage description information.

[0012] Step S122: Extract associated features from the state description information and linkage description information through the feature association layer of the sensor operation and maintenance analysis model to obtain state linkage associated features.

[0013] Step S1221: Perform key information extraction processing on the state description information to extract the core sensor operation state information from the state description information.

[0014] In this embodiment, for the status description information, such as the status description information of a pressure sensor including pressure detection values, operating time, and abnormal fluctuation alerts, an information extraction algorithm is used to filter out core information closely related to the sensor's operating status. This includes the rate of change of the pressure detection value, the equipment aging assessment index corresponding to the operating time, and the frequency and amplitude of abnormal fluctuations. This information constitutes the core status information of the sensor's operation. The information extraction algorithm performs semantic analysis on the status description information, identifying fields and data with key significance. For example, for the pressure detection value, it analyzes its comparison with historical detection values ​​and calculates the rate of change; for the operating time, it combines the designed service life of this type of sensor to calculate an assessment value of the remaining service life. These assessment values ​​and key data together constitute the core status information of the sensor's operation.

[0015] Step S1222: Extract key information from the linkage description information to extract the core interaction information of the device linkage. For example, the linkage description information between a flow sensor and a valve includes the valve's adjustment range, the flow change after adjustment, and the valve's operating status. Using an information extraction algorithm, core information closely related to the device linkage interaction is selected, such as the flow regulation efficiency corresponding to the valve's adjustment range, the stability index of the flow change, and the health assessment index of the valve's operating status. This information constitutes the core interaction information of the device linkage. The information extraction algorithm analyzes the linkage description information to identify key data affecting the device linkage effect and status. For example, for the valve's adjustment range, it analyzes its correspondence with flow change and calculates the regulation efficiency; for the valve's operating status, it combines historical fault data to assess its current health. These assessment values ​​and key data together constitute the core interaction information of the device linkage.

[0016] Step S1223: Input the core operating status information of the sensor and the core interaction information of the device linkage into the interaction analysis unit of the feature association layer for information interaction analysis processing to obtain interactive association information. In the interaction analysis unit of the feature association layer, the input core operating status information of the sensor and the core interaction information of the device linkage are analyzed for association. For example, the relationship between the pressure change rate in the core operating status information of the sensor and the valve regulation efficiency in the core interaction information of the device linkage is analyzed to determine whether the pressure change is caused by improper valve regulation; or the relationship between the temperature change trend of the temperature sensor and the operating power change of the cooling equipment is analyzed to determine whether the operation of the cooling equipment can effectively control temperature changes. The interaction analysis unit uses an association rule mining algorithm to find potential associations between the core operating status information of the sensor and the core interaction information of the device linkage. For example, when the pressure change rate exceeds a certain threshold, the probability that the valve regulation efficiency is lower than a certain standard value increases. These associations and analysis results together constitute the interactive association information.

[0017] Step S1224: Perform feature enhancement processing on the interactive related information to enhance the feature content related to operation and maintenance requirements in the interactive related information.

[0018] In this embodiment, the interactive association information includes the relationships between various information. For example, the core operational status information of a temperature sensor includes the temperature change rate and the equipment aging assessment value; the core interactive information of equipment linkage includes the operating power change of the cooling equipment and the temperature control effect. The feature enhancement algorithm adjusts the weights and enhances the information of features related to maintenance needs. For instance, for cases of poor temperature control, the weight of this feature in the interactive association information is increased, and information such as handling experience and solution suggestions from historical maintenance cases related to this situation is added to strengthen the importance of the feature. The feature enhancement algorithm assigns different weights to different features in the interactive association information according to the type and focus of the maintenance needs. The weight setting is based on the influence of the feature on the judgment of maintenance needs in historical maintenance data. By adjusting the weights and supplementing relevant information, the features related to maintenance needs are made more prominent.

[0019] Step S1225: Perform feature integration processing on the enhanced interactive association information to form state linkage association features. After feature enhancement, the enhanced interactive association information is integrated. For example, feature information of different dimensions is combined according to a certain logical structure to form a multi-dimensional feature set. This feature set includes the association features of the core sensor operation status information and the core device linkage interaction information, as well as the enhanced feature content related to operation and maintenance requirements. The feature integration process unifies the data structure and standardizes the format of the enhanced interactive association information, splicing feature information from different sources to form a complete state linkage association feature. This feature exists in the form of vectors or structured data.

[0020] Step S123: Use the demand mapping layer of the sensor operation and maintenance analysis model to perform preliminary matching of operation and maintenance requirements on the state linkage correlation features to obtain multiple preliminary operation and maintenance requirement items.

[0021] Step S1231: Retrieve the preset operation and maintenance requirement feature library in the requirement mapping layer. The operation and maintenance requirement feature library contains standard features corresponding to various operation and maintenance requirements.

[0022] In this embodiment, the operation and maintenance requirement feature library is built based on a large number of chemical production sensor operation and maintenance cases. It includes standard features corresponding to various operation and maintenance requirements such as equipment maintenance, parameter calibration, component replacement, and system optimization. For example, the standard features corresponding to equipment maintenance requirements include the frequency of equipment malfunctions, the degree of deviation of key parameters, and abnormal situations of equipment linkage; the standard features corresponding to parameter calibration requirements include the deviation rate between parameter detection values ​​and standard values, and the stability index of parameter changes.

[0023] Step S1232: Compare the status linkage association features with the standard features corresponding to various operation and maintenance requirements in the operation and maintenance requirement feature library one by one.

[0024] In this embodiment, for state-linked correlation features, such as the state-linked correlation features of a temperature sensor including temperature change rate, cooling equipment operating power change, and temperature control effect, these features are compared one by one with the standard features of various operation and maintenance requirements in the operation and maintenance requirement feature library. The comparison process calculates the similarity between the state-linked correlation features and each standard feature. The similarity calculation is based on the distance or correlation algorithm between feature vectors, such as the cosine similarity algorithm, which measures their similarity by calculating the cosine value of the angle between two feature vectors.

[0025] Step S1233: Mark the operation and maintenance requirements corresponding to the standard features that meet the requirements of the state linkage associated features.

[0026] In this embodiment, a matching threshold is set; for example, a matching degree greater than or equal to 80% is considered to meet the requirements. When the similarity calculation result between a state linkage association feature and a certain standard feature is greater than or equal to the threshold, the maintenance requirement corresponding to the standard feature is marked. For example, when the matching degree between a certain state linkage association feature and the standard feature of equipment maintenance requirement is 85%, the equipment maintenance requirement is marked.

[0027] Step S1234: Perform content integrity processing on the marked operation and maintenance requirements, and remove operation and maintenance requirements with missing content.

[0028] In this embodiment, for the marked maintenance requirements, we check whether their contents are complete. For example, does the equipment repair requirement include information such as the equipment components that need to be repaired, the time requirements for repair, and the required tools and materials? If the content of a maintenance requirement is incomplete, such as only stating that repair is needed but not specifying the specific equipment components and time requirements, then the maintenance requirement is removed to ensure the completeness of the contents of the subsequent preliminary maintenance requirement items.

[0029] Step S1235: The remaining maintenance requirements after processing are taken as multiple preliminary maintenance requirement items. After the above steps, the remaining maintenance requirements are multiple preliminary maintenance requirement items, which may include equipment maintenance requirements, parameter calibration requirements, etc.

[0030] Step S124: Through the relationship mining layer of the sensor operation and maintenance analysis model, perform interaction analysis on multiple preliminary operation and maintenance requirements to obtain a description of the degree of mutual influence between the preliminary operation and maintenance requirements.

[0031] In this embodiment, the relationship mining layer analyzes multiple preliminary maintenance requirements, such as the relationship between equipment maintenance requirements and parameter calibration requirements, to determine whether equipment maintenance will affect the accuracy of parameters, or whether parameter calibration will affect the operating status of the equipment. Through association analysis algorithms, it mines causal, synergistic, or conflicting relationships among the preliminary maintenance requirements. For example, if a component of the equipment is damaged (equipment maintenance requirement), it may lead to inaccurate detection of related parameters (triggering factor for parameter calibration requirement), thus establishing a causal relationship between these two requirements. The relationship mining layer calculates the degree of mutual influence between the preliminary maintenance requirements. For instance, by analyzing the processing effects and impacts of different requirements occurring simultaneously in historical maintenance data, it determines the degree of mutual influence indicators, such as the weight of the influence and the time range of the influence. These indicators collectively constitute a description of the degree of mutual influence between the preliminary maintenance requirements.

[0032] Step S125: Based on the degree of mutual influence between the preliminary maintenance requirements, construct the maintenance requirement association relationship corresponding to the IoT sensor cluster. The maintenance requirement association relationship includes the association order and association priority of each preliminary maintenance requirement item.

[0033] In this embodiment, the association order and priority of each preliminary maintenance requirement are determined based on the description of the degree of mutual influence between the requirements. For example, if equipment maintenance requirements affect the execution effect of parameter calibration requirements, and the urgency of equipment maintenance requirements is higher, then the association priority of equipment maintenance requirements is set to be higher than that of parameter calibration requirements, and the association order is set to execute equipment maintenance requirements before parameter calibration requirements.

[0034] Step S130: Integrate the operation and maintenance requirements of the IoT sensor cluster according to the relationship of the operation and maintenance requirements corresponding to the IoT sensor cluster to form a set of operation and maintenance requirements for the IoT sensor cluster.

[0035] In this embodiment, based on the maintenance requirement associations obtained in step S125, the various preliminary maintenance requirement items are integrated according to their association order and priority to form a complete maintenance requirement set. For example, equipment maintenance requirements, parameter calibration requirements, and component replacement requirements are arranged in descending order of priority and in descending order of execution, and integrated into the maintenance requirement set. This set contains detailed requirements for all maintenance work that needs to be performed in the IoT sensor cluster, and each requirement item includes information such as the type of requirement, execution conditions, associated sensors and devices, etc.

[0036] Step S131: Classify and filter each preliminary operation and maintenance requirement item in the operation and maintenance requirement association relationship corresponding to the IoT sensor cluster, and divide it into sensor self-operation and maintenance requirement items and device linkage operation and maintenance requirement items.

[0037] In the chemical production plant scenario of this embodiment, the preliminary maintenance requirements in the relationship of maintenance needs, such as equipment repair requirements, parameter calibration requirements, component replacement requirements, and system optimization requirements, are categorized and filtered. Specifically, sensor self-maintenance requirements refer to requirements that only involve the sensor itself, such as the parameter calibration requirement for a temperature sensor, which mainly involves calibrating the sensor's own detection parameters and does not involve the coordinated maintenance of other equipment; or the component replacement requirement for a pressure sensor, which mainly involves replacing damaged components of the pressure sensor. Equipment-coordinated maintenance requirements refer to requirements that involve the coordinated maintenance of sensors with other equipment, such as the coordinated maintenance requirement between a temperature sensor and cooling equipment, which requires not only the maintenance of the temperature sensor but also the inspection and maintenance of the linked cooling equipment; or the coordinated optimization requirement between a flow sensor and valves, which requires simultaneous optimization and adjustment of both the flow sensor and the linked valves. By analyzing the content of each preliminary maintenance requirement, it is determined whether it belongs to a sensor self-maintenance requirement or an equipment-coordinated maintenance requirement, and then it is categorized accordingly.

[0038] Step S132: Supplement the content of the sensor's own operation and maintenance requirements, and supplement the basic operation and maintenance element information required for the sensor's own operation and maintenance.

[0039] For example, step S1321: Extract the sensor type and abnormal operation information recorded in the sensor's own operation and maintenance requirements.

[0040] In this embodiment, for each sensor's own operation and maintenance requirements, such as the parameter calibration requirement of a temperature sensor, the sensor type (e.g., PT100 temperature sensor) and abnormal operation performance information (e.g., the deviation of the detected value from the standard value exceeds the allowable range, or the detected value fluctuates greatly) are extracted; for the component replacement requirement of a pressure sensor, the sensor type (e.g., diffused silicon pressure sensor) and abnormal operation performance information (e.g., the sensor displays a fault code, or the detected value does not change) are extracted.

[0041] Step S1322: Retrieve the corresponding sensor basic operation and maintenance specifications based on the extracted sensor type. The sensor basic operation and maintenance specifications include the necessary elements for routine operation and maintenance of the sensor corresponding to the extracted sensor type.

[0042] In this embodiment, the basic operation and maintenance specifications for sensors are stored in the database of the operation and maintenance management platform. Different types of sensors have corresponding basic operation and maintenance specifications. For example, for the PT100 temperature sensor, its basic operation and maintenance specifications include routine parameter calibration procedures (such as calibration steps, calibration instruments used, and standard calibration value ranges), sensor cleaning and maintenance requirements (such as cleaning cycles, cleaning reagents used, and cleaning methods), and sensor fault diagnosis methods (such as common fault phenomena, troubleshooting steps, and solutions). For the diffused silicon pressure sensor, its basic operation and maintenance specifications include routine component replacement procedures (such as replacement component model requirements, replacement steps, and post-replacement debugging methods), sensor calibration requirements (such as pressure calibration range and calibration accuracy requirements), and daily sensor inspection items (such as checking for loose sensor connections and damaged housings). Based on the extracted sensor type, the corresponding sensor basic operation and maintenance specifications are retrieved from the database.

[0043] Step S1323: Select targeted basic operation and maintenance elements from the sensor basic operation and maintenance specifications based on the information on abnormal operation performance.

[0044] In this embodiment, for the extracted abnormal operation information, such as the deviation of the temperature sensor's detected value from the standard value exceeding the allowable range, targeted basic operation and maintenance elements are selected based on the basic operation and maintenance specifications of the PT100 temperature sensor. These specifications include elements related to parameter calibration, such as calibration steps, calibration instruments used, and calibration standard value ranges. Based on the detected value deviation, targeted basic operation and maintenance elements are selected, including specific steps requiring parameter calibration, the high-precision calibration instruments used, and the calibration standard value range (e.g., 0-500℃). For the fault code display of the pressure sensor, based on the basic operation and maintenance specifications of the diffused silicon pressure sensor, relevant elements for fault diagnosis are selected, such as the fault type corresponding to the fault code, troubleshooting steps, and solutions.

[0045] Step S1324: Add the selected targeted basic operation and maintenance elements to the corresponding sensor's own operation and maintenance requirements.

[0046] In this embodiment, the selected basic maintenance elements, such as the steps, calibration instruments, and standard value ranges for temperature sensor parameter calibration, are added to the corresponding temperature sensor parameter calibration requirement item; the steps and solutions for pressure sensor fault diagnosis are added to the corresponding pressure sensor component replacement requirement item (because component replacement requirements may be due to faults, requiring fault diagnosis first). This addition process adds these basic maintenance elements to the sensor's own maintenance requirement item in a structured form, making the requirement item more complete and specific.

[0047] Step S1325: Integrate and connect the supplemented sensor self-maintenance requirements to make the basic maintenance element information and the original requirements form a coherent logic.

[0048] In this embodiment, for the supplemented sensor maintenance requirements, such as the parameter calibration requirement for a temperature sensor, the original requirement might have only stated that parameter calibration was needed. The supplemented requirement includes the calibration steps, calibration instrument, and standard value range. These contents are integrated and connected, for example, by organizing the original requirement and the supplemented basic maintenance information according to the calibration steps, forming a coherent logic, such as "Parameter calibration of the PT100 temperature sensor is required. The calibration steps are: 1. Connect the calibration instrument; 2. Input the standard value range (0-500℃); 3. Perform the calibration operation; ...". This makes the logic of the requirement clear.

[0049] Step S133: Supplement the content of the equipment linkage operation and maintenance requirements, and supplement the linkage operation and maintenance element information required for equipment linkage operation and maintenance.

[0050] In this embodiment, supplementary information is provided for equipment linkage maintenance requirements, such as the linkage maintenance requirements between temperature sensors and cooling equipment, and the linkage optimization requirements between flow sensors and valves. Taking the linkage maintenance requirement between temperature sensors and cooling equipment as an example, the linkage maintenance element information includes the maintenance requirements of the cooling equipment (such as the troubleshooting steps, maintenance cycle, and tools used), the linkage logic requirements between the temperature sensor and the cooling equipment (such as the linkage triggering conditions, linkage response time, and linkage control parameters), and the collaborative maintenance process between the two (such as whether to maintain the temperature sensor or the cooling equipment first, and the linkage testing steps after maintenance). By analyzing the content of the equipment linkage maintenance requirements and combining them with relevant equipment linkage maintenance specifications, these linkage maintenance element information are supplemented, making the content of the equipment linkage maintenance requirements more complete.

[0051] Step S134: Sort the supplemented sensor self-maintenance requirements and the supplemented device linkage maintenance requirements according to the association order and association priority in the maintenance requirement association relationship.

[0052] In this embodiment, the supplemented sensor self-maintenance requirements and equipment linkage maintenance requirements are sorted according to the association order and priority in the maintenance requirement association relationship obtained in step S125. For example, equipment linkage maintenance requirements with high association priority (such as the linkage maintenance requirement between a temperature sensor and a cooling device, which involves production safety and has a higher priority) will be ranked first, while sensor self-maintenance requirements with low association priority (such as the parameter calibration requirement of a pressure sensor, which has a relatively small impact on production) will be ranked later; the association order is such that equipment linkage maintenance requirements are executed before some sensor self-maintenance requirements, and the sorting is carried out according to this order.

[0053] Step S135: Integrate the sorted and supplemented sensor self-maintenance requirements and the supplemented device linkage maintenance requirements to form a set of maintenance requirements for the IoT sensor cluster.

[0054] In this embodiment, all sorted maintenance requirements are integrated to form a complete set of maintenance requirements for an IoT sensor cluster. This set includes detailed information on the maintenance requirements of the sensors themselves and the maintenance requirements of device linkage. Each requirement includes the type of requirement, execution conditions, associated sensors and devices, maintenance element information, etc.

[0055] Step S140: Generate an operation and maintenance resource configuration scheme adapted to the IoT sensor cluster by associating the set of operation and maintenance requirements of the IoT sensor cluster with the preset operation and maintenance resource pool information.

[0056] Step S141: Retrieve preset operation and maintenance resource pool information, which includes operation and maintenance personnel information, operation and maintenance tool information, and operation and maintenance material information.

[0057] In this embodiment, the operation and maintenance resource pool information is stored in the database of the operation and maintenance management platform. The operation and maintenance personnel information includes each operator's name, skill level, work experience, and current work status. The operation and maintenance tool information includes the name, model, applicable operation type, and current usage status of each tool. The operation and maintenance material information includes the name, specifications, inventory quantity, and storage location of each material. When an operation and maintenance resource configuration plan needs to be generated, this operation and maintenance resource pool information is retrieved from the database.

[0058] Step S142: Perform adaptation analysis and processing on each operation and maintenance requirement item in the set of operation and maintenance requirements of the IoT sensor cluster and the operation and maintenance personnel information, and filter out the target operation and maintenance personnel information that is compatible with each operation and maintenance requirement item.

[0059] Step S1421: Extract the operation and maintenance skill requirements corresponding to each operation and maintenance requirement item in the operation and maintenance requirement set of the IoT sensor cluster.

[0060] In this embodiment, for each maintenance requirement, such as equipment repair, it is necessary to extract its corresponding maintenance skill requirements. These requirements might include understanding the equipment's structural principles, possessing fault diagnosis capabilities, and being familiar with the use of relevant tools. The maintenance skill requirements for parameter calibration requirements include operating parameter testing instruments, data analysis capabilities, and mastery of calibration standards. By analyzing the content of each maintenance requirement, the corresponding maintenance skill requirements are extracted.

[0061] Step S1422: Retrieve the skill mastery information for each operations and maintenance personnel from the personnel information.

[0062] In this embodiment, the skill mastery information in the operation and maintenance personnel information includes the types of skills mastered by each operation and maintenance personnel, the level of skill proficiency, and relevant certificates obtained. For example, an operation and maintenance personnel who is skilled in the repair of pressure sensors, familiar with the calibration process of temperature sensors, and possesses relevant professional qualification certificates constitutes the skill mastery information of that operation and maintenance personnel.

[0063] Step S1423: Compare the operation and maintenance skill requirements corresponding to each operation and maintenance requirement with the skill mastery information of each operation and maintenance personnel.

[0064] In this embodiment, the maintenance skills requirements for each maintenance requirement, such as the maintenance skills requirements for equipment repair (including understanding of equipment structure and principles, and fault diagnosis capabilities), are compared with the skill mastery information of each maintenance personnel to determine whether their skills meet the requirements. The comparison process involves matching each skill requirement one by one; for example, regarding the requirement of understanding equipment structure and principles, it checks whether the maintenance personnel possess the relevant knowledge and experience.

[0065] Step S1424: Filter out maintenance personnel whose skill mastery information meets the corresponding maintenance skill requirements.

[0066] In this embodiment, an operations and maintenance (O&M) personnel is selected when their skill level information meets all the O&M skill requirements of the O&M needs. For example, if an O&M personnel's skill level information includes knowledge of equipment structure and principles, fault diagnosis capabilities, and proficiency in using relevant tools, and this information fully matches the O&M skill requirements for equipment maintenance, then that O&M personnel is selected.

[0067] Step S1425: Collect detailed information on the selected maintenance personnel to form target maintenance personnel information that matches each maintenance requirement.

[0068] In this embodiment, detailed information such as the name, contact information, current work status, and work experience of the selected maintenance personnel is collected. This information is then organized according to the corresponding maintenance requirements to form target maintenance personnel information. For example, the target maintenance personnel information corresponding to equipment maintenance requirements includes detailed information of maintenance personnel A, detailed information of maintenance personnel B, etc.

[0069] Step S143: Perform adaptation analysis and processing on each operation and maintenance requirement item and operation and maintenance tool information in the operation and maintenance requirement set of the IoT sensor cluster, and filter out the target operation and maintenance tool information that is compatible with each operation and maintenance requirement item.

[0070] Step S1431: Analyze the operation type corresponding to each operation and maintenance requirement item in the operation and maintenance requirement set of the IoT sensor cluster.

[0071] In this embodiment, for each maintenance requirement, such as equipment repair requirements, the corresponding maintenance operation types include equipment disassembly, fault detection, and component replacement; while the corresponding maintenance operation types for parameter calibration requirements include parameter detection, data adjustment, and calibration verification. By analyzing the content of each maintenance requirement, its corresponding maintenance operation type is determined.

[0072] Step S1432: Extract the applicable operation type information for each operation and maintenance tool from the operation and maintenance tool information.

[0073] In this embodiment, the applicable operation type information in the operation and maintenance tool information includes the operation types that each operation and maintenance tool can support. For example, a wrench is suitable for equipment disassembly and component installation operations, and a calibrator is suitable for parameter detection and calibration operations. By analyzing the content of the operation and maintenance tool information, its corresponding applicable operation type information is extracted.

[0074] Step S1433: Match the operation type corresponding to each operation and maintenance requirement with the applicable operation type information corresponding to each operation and maintenance tool.

[0075] In this embodiment, for each maintenance requirement item's maintenance operation type, such as equipment disassembly for equipment repair requirements, the operation type is matched with the applicable operation type information of each maintenance tool to determine whether the maintenance tool is suitable for the operation of that maintenance requirement item. The matching process checks each operation type one by one; for example, for equipment disassembly operations, it checks whether the maintenance tools include tools suitable for that operation.

[0076] Step S1434: Confirm the current usage status of the matched operation and maintenance tools, and remove operation and maintenance tools that cannot be used at present.

[0077] In this embodiment, the current usage status of the matched maintenance tools is checked, such as whether they are currently in use, under maintenance, or have any fault indications. If a maintenance tool is currently unavailable, for example, if it is being used by other maintenance tasks or is malfunctioning, then that maintenance tool is removed.

[0078] Step S1435: Summarize the relevant information of the currently available and successfully matched operation and maintenance tools to form target operation and maintenance tool information that adapts to each operation and maintenance requirement.

[0079] In this embodiment, information such as the name, model, and storage location of currently available and successfully matched maintenance tools is collected and organized according to the corresponding maintenance requirements to form target maintenance tool information. For example, the target maintenance tool information corresponding to equipment maintenance requirements includes information on tools such as wrenches, screwdrivers, and fault diagnostic instruments.

[0080] Step S144: Perform adaptation analysis on each operation and maintenance requirement item in the set of operation and maintenance requirements of the IoT sensor cluster and the operation and maintenance tool information, and filter out the target operation and maintenance tool information that is compatible with each operation and maintenance requirement item.

[0081] Step S1441: Parse the operation type corresponding to each operation and maintenance requirement item in the set of operation and maintenance requirements of the IoT sensor cluster. This step has the same processing logic as step S1431. For example, for parameter calibration requirements, the corresponding operation and maintenance type is parsed to include parameter detection, data adjustment, calibration verification, etc.

[0082] Step S1442: Extract the applicable operation type information for each operation and maintenance tool from the operation and maintenance tool information. This step has the same processing logic as step S1432. For example, extracting the applicable operation type information for the calibrator includes parameter detection, calibration operations, etc.

[0083] Step S1443: Match the operation type corresponding to each operation and maintenance requirement with the applicable operation type information corresponding to each operation and maintenance tool. This step has the same processing logic as step S1433. For example, match the operation and maintenance operation type of parameter calibration requirement with the applicable operation type information of the calibrator to determine whether the calibrator is suitable for the operation of the requirement.

[0084] Step S1444: Confirm the current usage status of the matched maintenance tools and remove those that are currently unusable. This step follows the same processing logic as step S1434. For example, check the current usage status of the calibrator; if it is idle and fault-free, retain the tool.

[0085] Step S1445: Summarize the relevant information of currently available and successfully matched operation and maintenance tools to form target operation and maintenance tool information adapted to each operation and maintenance requirement. This step has the same processing logic as step S1435, for example, collecting relevant information of tools such as calibrators and data analyzers to form target operation and maintenance tool information corresponding to parameter calibration requirements.

[0086] Step S145: Perform adaptation analysis on each operation and maintenance requirement item and operation and maintenance material information in the operation and maintenance requirement set of the IoT sensor cluster, and filter out the target operation and maintenance material information that is compatible with each operation and maintenance requirement item.

[0087] Step S1451: Analyze the maintenance material requirements corresponding to each maintenance requirement item in the maintenance requirement set of the IoT sensor cluster.

[0088] In this embodiment, for each maintenance requirement, such as equipment repair, it is necessary to analyze its corresponding maintenance material requirements, such as the equipment parts to be replaced and the consumables required for repair; the maintenance material requirements corresponding to parameter calibration requirements include standard substances for calibration, printing paper, etc. By analyzing the content of the maintenance requirement items, their corresponding maintenance material requirements are determined.

[0089] Step S1452: Retrieve the inventory status and specification parameters of each maintenance material from the maintenance material information.

[0090] In this embodiment, the maintenance material information includes the inventory quantity, storage location, and specifications of each maintenance material. For example, the inventory quantity of a certain equipment component is 5 units, stored in warehouse A area, and the specifications meet the requirements of a certain model of equipment; the inventory quantity of a certain standard substance is 10 bottles, and the specifications meet the requirements of a certain calibration requirement.

[0091] Step S1453: Match the maintenance material requirements corresponding to each maintenance requirement with the inventory status and specification parameters of each maintenance material.

[0092] In this embodiment, for each maintenance requirement, such as the specifications of the equipment parts to be replaced during equipment repair, the required maintenance materials are matched with the specifications of each maintenance material, while simultaneously checking whether the inventory meets the requirements. The matching process compares the specifications one by one. For example, for the specifications of equipment parts, it checks whether their model, size, performance indicators, etc., are consistent with the requirements; for inventory, it checks whether the inventory quantity is greater than or equal to the required quantity.

[0093] Step S1454: Filter out maintenance materials whose inventory status and specification parameters match the corresponding maintenance material requirements.

[0094] In this embodiment, the maintenance material is selected when both its inventory status and specifications meet the maintenance material requirements. For example, if the specifications of a certain equipment component match the equipment maintenance requirements and its inventory quantity is greater than or equal to the required quantity, then that equipment component is selected.

[0095] Step S1455: Summarize the relevant information of the selected maintenance materials to form target maintenance material information that matches each maintenance requirement.

[0096] In this embodiment, information such as the name, specifications, inventory quantity, and storage location of the selected maintenance materials is collected and organized according to the corresponding maintenance requirements to form target maintenance material information. For example, the target maintenance material information corresponding to equipment maintenance requirements includes information on equipment components, maintenance consumables, etc.

[0097] Step S146: Combine the target maintenance personnel information, target maintenance tool information, and target maintenance material information according to the corresponding maintenance requirements to generate an maintenance resource configuration scheme adapted to the IoT sensor cluster.

[0098] In this embodiment, the target maintenance personnel information, target maintenance tool information, and target maintenance material information corresponding to each maintenance requirement are combined to form a complete maintenance resource configuration scheme. For example, for equipment repair requirements, the corresponding target maintenance personnel information, target maintenance tool information (such as wrenches, fault diagnostic instruments, etc.), and target maintenance material information (such as equipment parts, maintenance consumables, etc.) are combined to form a resource configuration sub-scheme for that requirement; for parameter calibration requirements, the corresponding target maintenance personnel information, target maintenance tool information (such as calibrators, data analyzers, etc.), and target maintenance material information (such as standard substances, printing paper, etc.) are combined to form a resource configuration sub-scheme for that requirement. By integrating the resource configuration sub-schemes for all maintenance requirements, a maintenance resource configuration scheme adapted to IoT sensor clusters is obtained. This scheme contains detailed resource configuration information for each maintenance requirement, providing comprehensive guidance for maintenance execution.

[0099] Step S150: Push the operation and maintenance resource configuration scheme adapted to the IoT sensor cluster to the operation and maintenance execution terminal and receive the operation and maintenance scheme execution feedback information returned by the operation and maintenance execution terminal.

[0100] Step S151: Standardize the format of the operation and maintenance resource configuration scheme adapted to the IoT sensor cluster and generate an operation and maintenance scheme file that conforms to the receiving specifications of the operation and maintenance execution end.

[0101] In this embodiment, the receiving specifications of the operation and maintenance execution end include file format requirements, data structure requirements, etc., such as requiring the file to be in XML format and containing specific tags and fields. A format conversion tool is used to convert the operation and maintenance resource configuration scheme into an operation and maintenance scheme file that conforms to this specification. For example, the resource configuration information for each operation and maintenance requirement item is organized according to XML tags to generate a corresponding XML file.

[0102] Step S152: Push the operation and maintenance plan file to the operation and maintenance execution terminal through the IoT communication link.

[0103] In this embodiment, the generated operation and maintenance plan file is sent to the operation and maintenance execution end using an IoT communication network, such as to the mobile terminal or workstation of the operation and maintenance personnel. The communication link adopts encrypted transmission to ensure the security and integrity of the data, for example, by using SSL / TLS encryption protocol to encrypt the transmitted data and prevent the data from being stolen or tampered with during transmission.

[0104] Step S153: Continuously receive operation and maintenance execution progress information transmitted from the operation and maintenance execution terminal.

[0105] In this embodiment, the operation and maintenance execution terminal transmits operation and maintenance progress information to the operation and maintenance management platform in real time or at set time intervals. This information includes details such as whether operation and maintenance personnel have arrived on-site, are performing equipment maintenance, or have completed a certain stage of work. This progress information is transmitted in the form of data packets, which are then parsed by the information receiving module of the operation and maintenance management platform to update the progress status of the operation and maintenance work in real time.

[0106] Step S154: After the operation and maintenance execution terminal completes the operation and maintenance operation, receive the operation and maintenance result details information sent by the operation and maintenance execution terminal.

[0107] In this embodiment, after the operation and maintenance execution terminal completes the operation and maintenance operation, it will send operation and maintenance result details to the operation and maintenance management platform, such as the results of equipment maintenance (whether the fault has been eliminated, whether the equipment operation status has returned to normal, etc.), the results of parameter calibration (calibrated parameter values, accuracy assessment of calibration, etc.), and the results of component replacement (operation status of the replaced equipment, etc.).

[0108] Step S155: Integrate the operation and maintenance execution progress information and operation and maintenance result details information to form operation and maintenance plan execution feedback information.

[0109] In this embodiment, the continuously received operation and maintenance execution progress information and the final operation and maintenance result details are integrated to form a complete operation and maintenance plan execution feedback information. The integration process associates the progress information and the result information. For example, the execution progress information of a certain operation and maintenance requirement item is matched with its corresponding result details to form the complete feedback information for that requirement item. Then, the feedback information of all requirement items is integrated to form the execution feedback information of the entire operation and maintenance plan.

[0110] Figure 2 The illustration shows exemplary hardware and software components of an IoT-integrated sensor intelligent operation and maintenance service optimization system 100, which can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the IoT-integrated sensor intelligent operation and maintenance service optimization system 100 and to perform the functions in this application.

[0111] The IoT-integrated sensor intelligent operation and maintenance service optimization system 100 can be a general-purpose server or a special-purpose server; both can be used to implement the IoT-integrated sensor intelligent operation and maintenance service optimization method of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the load.

[0112] For example, the IoT-integrated sensor-based intelligent operation and maintenance service optimization system 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the IoT-integrated sensor-based intelligent operation and maintenance service optimization system 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The IoT-integrated sensor-based intelligent operation and maintenance service optimization system 100 also includes an I / O interface 150 between the computer and other input / output devices.

[0113] For ease of explanation, only one processor is described in the IoT-integrated sensor intelligent operation and maintenance service optimization system 100. However, it should be noted that the IoT-integrated sensor intelligent operation and maintenance service optimization system 100 may also include multiple processors. Therefore, the steps performed by one processor as described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the IoT-integrated sensor intelligent operation and maintenance service optimization system 100 performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually by one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0114] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned sensor intelligent operation and maintenance service optimization method combined with the Internet of Things is implemented.

[0115] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for optimizing intelligent operation and maintenance services for sensors combined with the Internet of Things, characterized in that, The method includes: Receive real-time sensor operation feedback information transmitted by the Internet of Things sensor cluster. The real-time sensor operation feedback information includes the sensor's operation status feedback content and the linkage feedback content of the sensor's associated devices. The pre-trained sensor operation and maintenance analysis model is invoked to mine and process the operation and maintenance requirements of the real-time operation feedback information of the sensors, and generate the operation and maintenance requirement association relationship corresponding to the IoT sensor cluster. The operation and maintenance requirements of the IoT sensor cluster are integrated based on the relationship between the operation and maintenance requirements of the IoT sensor cluster. Based on the set of operation and maintenance requirements of IoT sensor clusters, and the information of the preset operation and maintenance resource pool, an operation and maintenance resource configuration scheme adapted to IoT sensor clusters is generated. Push the operation and maintenance resource configuration plan adapted to the IoT sensor cluster to the operation and maintenance execution terminal and receive the operation and maintenance plan execution feedback information returned by the operation and maintenance execution terminal.

2. The method for optimizing intelligent operation and maintenance services for sensors combined with the Internet of Things according to claim 1, characterized in that, The process involves calling a pre-trained sensor operation and maintenance analysis model to mine and process the operation and maintenance requirements of the real-time operation feedback information of the sensors, generating the operation and maintenance requirement associations corresponding to the IoT sensor cluster, including: The real-time operation feedback information of the sensor is input into the feedback information parsing layer of the sensor operation and maintenance analysis model to perform layered parsing processing on the real-time operation feedback information of the sensor, and to obtain the status description information corresponding to the sensor operation status feedback content and the linkage description information corresponding to the linkage feedback content of the sensor-associated equipment. The sensor operation and maintenance analysis model uses a feature association layer to extract association features from the state description information and linkage description information to obtain state linkage association features. The demand mapping layer of the sensor operation and maintenance analysis model is used to perform preliminary matching of operation and maintenance requirements on the state linkage correlation features to obtain multiple preliminary operation and maintenance requirement items. By using the relationship mining layer of the sensor operation and maintenance analysis model, the interaction analysis of multiple preliminary operation and maintenance requirements is performed to obtain a description of the degree of mutual influence between the preliminary operation and maintenance requirements. The relationship between maintenance requirements for an IoT sensor cluster is constructed based on the degree of mutual influence between the initial maintenance requirements. This relationship includes the association order and priority of each initial maintenance requirement.

3. The sensor intelligent operation and maintenance service optimization method combined with the Internet of Things as described in claim 1, characterized in that, The process of integrating the operation and maintenance requirements of the IoT sensor cluster based on the correlation of the corresponding operation and maintenance requirements includes: The initial maintenance requirements in the relationship between maintenance requirements of IoT sensor clusters are classified and filtered to separate the sensor’s own maintenance requirements and the device linkage maintenance requirements. The content of the sensor’s own operation and maintenance requirements is supplemented to include basic operation and maintenance element information required for the sensor’s own operation and maintenance. Supplement the equipment linkage operation and maintenance requirements by adding information on the linkage operation and maintenance elements required for equipment linkage operation and maintenance. The supplemented sensor self-maintenance requirements and the supplemented device linkage maintenance requirements are sorted according to the association order and association priority in the maintenance requirement association relationship; The sorted and supplemented sensor self-maintenance requirements and the supplemented device linkage maintenance requirements are integrated to form the maintenance requirements set of the Internet of Things sensor cluster.

4. The sensor intelligent operation and maintenance service optimization method combined with the Internet of Things as described in claim 1, characterized in that, The set of operation and maintenance requirements based on the IoT sensor cluster is associated with a preset operation and maintenance resource pool information to generate an operation and maintenance resource configuration scheme adapted to the IoT sensor cluster, including: Retrieve preset operation and maintenance resource pool information, which includes operation and maintenance personnel information, operation and maintenance tool information, and operation and maintenance material information; The operation and maintenance requirements of the IoT sensor cluster are matched and analyzed with the operation and maintenance personnel information to select the target operation and maintenance personnel information that matches each operation and maintenance requirement. The operation and maintenance requirements of the IoT sensor cluster are matched and analyzed with the operation and maintenance tool information to select the target operation and maintenance tool information that is compatible with each operation and maintenance requirement. The operation and maintenance requirements of the IoT sensor cluster are matched and analyzed with the operation and maintenance material information to select the target operation and maintenance material information that matches each operation and maintenance requirement. The target maintenance personnel information, target maintenance tool information, and target maintenance material information are combined and processed according to the corresponding maintenance requirements to generate an maintenance resource configuration scheme adapted to the IoT sensor cluster.

5. The sensor intelligent operation and maintenance service optimization method combined with the Internet of Things according to claim 2, characterized in that, The feature association layer of the sensor operation and maintenance analysis model extracts association features from the state description information and linkage description information to obtain state linkage association features, including: The key information extraction process is performed on the status description information to extract the core sensor operation status information from the status description information; The key information extraction process is performed on the linkage description information to extract the core interaction information of device linkage from the linkage description information; The core operational status information of the sensor and the core interactive information of the device linkage are input into the interactive analysis unit of the feature association layer for information interaction analysis and processing to obtain interactive association information. Perform feature enhancement processing on interactive and related information to strengthen the feature content in interactive and related information that is relevant to operation and maintenance needs; The enhanced interactive information is processed by feature integration to form state linkage features.

6. The sensor intelligent operation and maintenance service optimization method combined with the Internet of Things according to claim 2, characterized in that, The requirement mapping layer of the sensor operation and maintenance analysis model performs preliminary matching processing on the state linkage correlation features to obtain multiple preliminary operation and maintenance requirement items, including: Retrieve the preset operation and maintenance requirement feature library in the requirement mapping layer. The operation and maintenance requirement feature library contains standard features corresponding to various operation and maintenance requirements. The status linkage correlation features are compared one by one with the standard features corresponding to various operation and maintenance requirements in the operation and maintenance requirement feature library. Mark the operation and maintenance requirements corresponding to the standard features that meet the requirements for matching the status linkage associated features; Perform content integrity processing on the marked operation and maintenance requirements, and remove operation and maintenance requirements with missing content; The remaining maintenance requirements after processing are considered as several initial maintenance requirement items.

7. The sensor intelligent operation and maintenance service optimization method combined with the Internet of Things according to claim 4, characterized in that, The process of matching and analyzing the maintenance requirements of the IoT sensor cluster with maintenance personnel information to identify target maintenance personnel information that matches each maintenance requirement includes: Extract the corresponding operation and maintenance skill requirements for each operation and maintenance requirement item in the set of operation and maintenance requirements for IoT sensor clusters; Retrieve the skill proficiency information for each operations and maintenance personnel from the personnel information database; The operation and maintenance skill requirements corresponding to each operation and maintenance requirement are compared with the skill mastery information of each operation and maintenance personnel. Screen out maintenance personnel whose skill proficiency information matches the corresponding maintenance skill requirements; Collect detailed information on the selected operations and maintenance personnel to form target operations and maintenance personnel information that matches various operations and maintenance requirements.

8. The sensor intelligent operation and maintenance service optimization method combined with the Internet of Things according to claim 4, characterized in that, The process involves matching and analyzing the maintenance requirements of the IoT sensor cluster with maintenance tool information to identify target maintenance tools that fit each maintenance requirement. This includes: Analyze the operation type corresponding to each operation and maintenance requirement item in the operation and maintenance requirement set of the IoT sensor cluster; Extract the applicable operation type information for each operation and maintenance tool from the operation and maintenance tool information; Match the operation type corresponding to each operation and maintenance requirement with the applicable operation type information corresponding to each operation and maintenance tool; Confirm the current usage status of the matched operation and maintenance tools, and remove those that are currently unusable. Summarize the relevant information of currently available and successfully matched operation and maintenance tools to form target operation and maintenance tool information that is adapted to various operation and maintenance requirements.

9. The method for optimizing intelligent operation and maintenance services for sensors combined with the Internet of Things according to claim 1, characterized in that, The process of pushing the operation and maintenance resource configuration scheme adapted to the IoT sensor cluster to the operation and maintenance execution terminal and receiving the operation and maintenance scheme execution feedback information returned by the operation and maintenance execution terminal includes: The format of the operation and maintenance resource configuration scheme adapted to the IoT sensor cluster is standardized to generate an operation and maintenance scheme file that conforms to the receiving specifications of the operation and maintenance execution end. The operation and maintenance plan document is pushed to the operation and maintenance execution terminal through the IoT communication link; Continuously receive operation and maintenance execution progress information transmitted from the operation and maintenance execution terminal; After the operation and maintenance execution terminal completes the operation and maintenance operation, receive the operation and maintenance result details information sent by the operation and maintenance execution terminal; Integrate operation and maintenance execution progress information and operation and maintenance result details to form operation and maintenance plan execution feedback information.

10. A sensor-based intelligent operation and maintenance service optimization system integrating the Internet of Things, characterized in that, The IoT-integrated sensor intelligent operation and maintenance service optimization system includes a processor and a memory, the memory and the processor being connected. The memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the IoT-integrated sensor intelligent operation and maintenance service optimization method as described in any one of claims 1-9.

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